Rate allocation for serial concatenated convolutional codes

The paper proposes an analytical method to solve the rate allocation problem in serial concatenated convolutional codes (SCCCs). The goal is to find the best rate allocation between the inner and the outer constituent convolutional codes of an SCCC for constant overall code rate, interleaver size and complexity. Simulation results are shown in the paper to demonstrate the optimum and superior design criteria. In addition, the `density evolution' model is shown to verify the proposed rate allocation method, while indicating that a high rate inner code should not be used for SCCCs. Finally, it is also shown that the upper bounds on BER of ML decoded SCCCs do not provide good design criteria for allocating the rate in iteratively decoded SCCCs     

Synthesis of cobalt-orthotitanate inverse spinel nano particles via a novel low temperature solvothermal method: structural,opto-electronical,morphological, surface characterization and photo-catalytical application in mineralization of Remazol Red RB 133

Synthesis of single phase cobalt-orthotitanate inverse spinel nano particles is reported for the first time via a novel solvothermal method at low calcination temperature at 400?°C. The pure phase cobalt-orthotitanate spinel nano particles were prepared using cobalt nitrate and titanium tetraisopropoxide (1:1 molar ratio) as cobalt and titanium sources. The synthesised Co₂TiO₄ nanoparticles were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), fourier transform infrared spectroscopy, energy dispersive X-ray spectroscopy, surface area analyses (Brauner–Emmett–Teller) (BET) and UV–Vis diffuse reflectance spectroscopy. The XRD results proved the formation of a single phase cobalt-orthotitanate (Co₂TiO₄) nanoparticles by calcination temperature of 400?°C. The FE-SEM results showed that nano particles possess a uniform spherical morphology with an average size of 51 nm. Porosity and specific surface area of cobalt-orthotitanate nano particles was measured by nitrogen adsorption using BET and the results showed surface area of 44.47 m²/g. DRS results showed an optical band gap value of 1.802 eV for cobalt-orthotitanate nano powder. An excellent performance as a nano photo-catalyst toward the degradation of Remazol Red RB 133 (RR133) as a single azo textile dye with excellent efficiency. Mineralization of RR133 by highly active cobalt-orthotitanate nano-catalyst coated on glass surface was applied and 97% TOC removal was observed. This is due to high electron–hole charge separation and high surface area of nano-catalyst.     

Photoluminescence and UV photosensitivity of few-layered MoS2 nanosheets synthesized under different hydrothermal growth times

Journal of Materials Science - MoS2 flower-like microspheres composed of 2D nanosheets were prepared using the simple and cost-effective method of hydrothermal. The effects of hydrothermal process...     

The impacts of tomato residuum extract with Arabic gum and dill essential oil on the shelf life improvement of trout fillets stored at chilly condition

The objective of this study was to evaluate the effects of tomato residuum extract (TRE) dipping and Arabic gum (AG) coating enriched with dill essential oil (DEO) on the shelf life extension of refrigerated trout fillets. Gas chromatography/mass spectrometry analysis revealed that the main constituents of DEO were alpha-phellandrene (30.17%), limonene (28.31%), and carvone (21.31%). Antioxidant activities of acetone, ethanol, methanol, cold, and hot water extracts of tomato residuum were examined using reducing power and 2,2-azinobis-3-ethylbenzothiazoline-6-sulphonic acid assays in maceration, ultrasound, and combined ultrasound and maceration extraction methods. The strongest antioxidant activities were found in the ultrasound-assisted extraction with ethanol 50%. Thiobarbituric acid, total volatile basic nitrogen, and peroxide values indicated that TRE 3%–AG–DEO 2% and TRE 6%–AG–DEO 2% treatments could significantly (p ≤ .05) extend the shelf life of the fillets. Also, sensory evaluation showed that TRE along with DEO had significant (p ≤ .05) pleasant effects on the sensory characteristics of the fillets. It was concluded that TRE dipping along with AG coating containing DEO could be a suitable alternative for the synthetic preservative in the refrigerated trout fillets.     

Application of Taguchi,ANFIS and grey relational analysis for studying,modeling and optimization of wire EDM process while using gaseous media

In the dry wire electrical discharge machining (WEDM) process, the liquid dielectric is replaced with gaseous medium to enhance the machining environment safety. Also, this modification improves the surface quality of machined specimen but decreases the material removal rate of the process. In the present work, experimental study of dry WEDM process has been performed while machining of Al/SiC metal matrix composite. At first, a series of exploratory experiments has been conducted to identify appropriate gas and wire material based on their cutting velocity. After selection of the best gas and best wire, they were used for later stage of experiments. At the next stage, experiment were designed and conducted based on L₂₇ Taguchi's orthogonal array to study the effect of pulse on time, pulse off time, gap voltage, discharge current, wire tension and wire feed on cutting velocity (CV) and surface roughness (SR). Analysis of variances (ANOVA) has been performed to identify significant factors. In order to correlate relationship between process inputs and responses, adaptive neuro-fuzzy inference system has been utilized. At the end, a grey relational analysis has been used to maximize CV and minimize SR simultaneously. Results indicated that oxygen gas and brass wire guarantee superior cutting velocity. Also according to ANOVA, pulse on time and current were found to have significant effect on CV and SR.     

Artificial evolutionary approaches to produce smoother surface in magnetic abrasive finishing of hardened AISI 52100 steel

In this work, two models of feed forward back-propagation neural network (FFBP-NN) and adaptive neuro-fuzzy inference system (ANFIS) have been developed to predict the performance of magnetic abrasive finishing process, based on experimental data of literature [7]. Input parameters of process are electromagnet’s voltage, mesh number of abrasive particles, poles rotational speed and weight percent of abrasive particles, and also the output is percentage of surface roughness variation. In order to select the best model, a comparison between developed models has been done based on their mean absolute error (MAE) and root mean square error (RMSE). Moreover, optimization methods based on simulated annealing (SA) and particle swarm optimization (PSO) algorithms were used to maximize the percent of surface roughness variation and select the optimal process parameters. Results indicated that the models based on artificial intelligence predict much more precise values with respect to predictive regression model developed in main literature [7]. Also, the ANFIS model had a lowest value of MAE and RMSE with respect to others. So it was used as an objective function to maximize the surface roughness variation by using SA and PSO. Comparison between the obtained optimal solutions and analysis of results in main literature indicated that SA and PSO could find the optimal answers logically and precisely.     

A dynamic data dissemination mechanism for Cassandra NoSQL data store

The Journal of Supercomputing - Cassandra as a type of NoSQL databases has been put forward so as to surmount the hurdles of traditional relational databases in the scope of big data as well as...     

Improvement of dry EDM process characteristics using artificial soft computing methodologies

Dry electrical discharge machining (EDM) is an environmentally-friendly alternative of die-sinking EDM process, which it uses gaseous medium instead of liquid as a dielectric. Due to contribution of too many parameters in this process, selection of optimal parameters to increase the process performances is a really crucial concern. In this work, a predictive model based on back-propagation neural network has been applied to correlate the inputs and outputs of dry EDM process. Herein, the inputs were gap voltage, pulse current, pulse on time, duty factor, air intake pressure and rotational speed of tool, and also the main outputs were material removal rate (MRR) and surface roughness (SR). Firstly a back-propagation (BP) and radial basis function neural network have been developed based on data generated from literature [Saha and Choudhary Int J Mach Tools Manuf 49:297?C308 (2009)]. Then, the accuracy of proposed models has been checked by their values of error percent via testing data. Hereafter, the most accurate model was served as an objective function to optimize the process using artificial bee colony (ABC) algorithm. In optimization stage, firstly a single objective optimization was fulfilled to determine the optimal factors related to each output separately. Then a multi-objective optimization was implemented to calculate the best solutions in the case of higher MRR and lower SR simultaneously. Results indicated that the predictive model can estimate the dry EDM process precisely, and also the ABC algorithm could find the optimal solution sets logically.